Overview

Dataset statistics

Number of variables20
Number of observations346
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.9 KiB
Average record size in memory115.3 B

Variable types

Numeric11
Categorical6
Boolean3

Alerts

holiday has constant value "False" Constant
date has a high cardinality: 167 distinct values High cardinality
df_index is highly correlated with temp and 1 other fieldsHigh correlation
temp is highly correlated with df_index and 1 other fieldsHigh correlation
month is highly correlated with df_index and 1 other fieldsHigh correlation
df_index is highly correlated with monthHigh correlation
month is highly correlated with df_indexHigh correlation
df_index is highly correlated with rain and 2 other fieldsHigh correlation
rain is highly correlated with df_index and 1 other fieldsHigh correlation
temp is highly correlated with yearHigh correlation
rhum is highly correlated with yearHigh correlation
wdsp is highly correlated with yearHigh correlation
hour is highly correlated with yearHigh correlation
day is highly correlated with yearHigh correlation
month is highly correlated with df_index and 1 other fieldsHigh correlation
year is highly correlated with df_index and 7 other fieldsHigh correlation
year is highly correlated with holidayHigh correlation
season is highly correlated with holidayHigh correlation
dayofweek is highly correlated with peak and 2 other fieldsHigh correlation
peak is highly correlated with dayofweek and 2 other fieldsHigh correlation
timesofday is highly correlated with holidayHigh correlation
rain_type is highly correlated with holidayHigh correlation
working_day is highly correlated with dayofweek and 2 other fieldsHigh correlation
holiday is highly correlated with year and 6 other fieldsHigh correlation
df_index is highly correlated with temp and 3 other fieldsHigh correlation
rain is highly correlated with rain_typeHigh correlation
temp is highly correlated with df_index and 4 other fieldsHigh correlation
hour is highly correlated with peak and 1 other fieldsHigh correlation
day is highly correlated with temp and 1 other fieldsHigh correlation
month is highly correlated with df_index and 4 other fieldsHigh correlation
year is highly correlated with df_index and 1 other fieldsHigh correlation
dayofweek_n is highly correlated with dayofweek and 2 other fieldsHigh correlation
dayofweek is highly correlated with dayofweek_n and 2 other fieldsHigh correlation
working_day is highly correlated with dayofweek_n and 3 other fieldsHigh correlation
season is highly correlated with df_index and 3 other fieldsHigh correlation
peak is highly correlated with hour and 3 other fieldsHigh correlation
timesofday is highly correlated with hourHigh correlation
rain_type is highly correlated with rainHigh correlation
count_day is highly correlated with temp and 2 other fieldsHigh correlation
df_index has unique values Unique
rain has 341 (98.6%) zeros Zeros
dayofweek_n has 32 (9.2%) zeros Zeros

Reproduction

Analysis started2022-04-13 21:56:35.178023
Analysis finished2022-04-13 21:56:49.611964
Duration14.43 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2624.456647
Minimum18
Maximum8387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-13T22:56:49.710791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile179.25
Q1899.5
median2394
Q34119.5
95-th percentile6020.25
Maximum8387
Range8369
Interquartile range (IQR)3220

Descriptive statistics

Standard deviation1927.612368
Coefficient of variation (CV)0.7344805525
Kurtosis-0.6393591278
Mean2624.456647
Median Absolute Deviation (MAD)1566
Skewness0.5445751954
Sum908062
Variance3715689.443
MonotonicityStrictly increasing
2022-04-13T22:56:49.839916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181
 
0.3%
33251
 
0.3%
34421
 
0.3%
34271
 
0.3%
34041
 
0.3%
34021
 
0.3%
33991
 
0.3%
33751
 
0.3%
33741
 
0.3%
33471
 
0.3%
Other values (336)336
97.1%
ValueCountFrequency (%)
181
0.3%
421
0.3%
601
0.3%
831
0.3%
1121
0.3%
1291
0.3%
1311
0.3%
1321
0.3%
1331
0.3%
1351
0.3%
ValueCountFrequency (%)
83871
0.3%
81771
0.3%
81541
0.3%
78181
0.3%
66881
0.3%
63971
0.3%
62721
0.3%
62281
0.3%
62271
0.3%
61821
0.3%

date
Categorical

HIGH CARDINALITY

Distinct167
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2021-04-24
 
7
2021-04-25
 
7
2021-03-07
 
7
2021-03-17
 
7
2021-04-03
 
7
Other values (162)
311 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)24.0%

Sample

1st row2021-03-01
2nd row2021-03-02
3rd row2021-03-03
4th row2021-03-04
5th row2021-03-05

Common Values

ValueCountFrequency (%)
2021-04-247
 
2.0%
2021-04-257
 
2.0%
2021-03-077
 
2.0%
2021-03-177
 
2.0%
2021-04-037
 
2.0%
2021-04-046
 
1.7%
2021-04-176
 
1.7%
2021-06-295
 
1.4%
2021-03-275
 
1.4%
2021-04-105
 
1.4%
Other values (157)284
82.1%

Length

2022-04-13T22:56:49.945138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-04-247
 
2.0%
2021-03-077
 
2.0%
2021-03-177
 
2.0%
2021-04-037
 
2.0%
2021-04-257
 
2.0%
2021-04-046
 
1.7%
2021-04-176
 
1.7%
2021-04-025
 
1.4%
2021-07-235
 
1.4%
2021-03-065
 
1.4%
Other values (157)284
82.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rain
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.008670520231
Minimum0
Maximum1.1
Zeros341
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-13T22:56:50.025480image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.1
Range1.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08570235718
Coefficient of variation (CV)9.884338529
Kurtosis130.9084714
Mean0.008670520231
Median Absolute Deviation (MAD)0
Skewness11.19828909
Sum3
Variance0.007344894027
MonotonicityNot monotonic
2022-04-13T22:56:50.106461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0341
98.6%
1.11
 
0.3%
11
 
0.3%
0.51
 
0.3%
0.11
 
0.3%
0.31
 
0.3%
ValueCountFrequency (%)
0341
98.6%
0.11
 
0.3%
0.31
 
0.3%
0.51
 
0.3%
11
 
0.3%
1.11
 
0.3%
ValueCountFrequency (%)
1.11
 
0.3%
11
 
0.3%
0.51
 
0.3%
0.31
 
0.3%
0.11
 
0.3%
0341
98.6%

temp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct163
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.48815029
Minimum3.3
Maximum26.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-13T22:56:50.205631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile5.575
Q110.1
median13.2
Q317.2
95-th percentile22.175
Maximum26.3
Range23
Interquartile range (IQR)7.1

Descriptive statistics

Standard deviation4.902079244
Coefficient of variation (CV)0.3634359893
Kurtosis-0.644429132
Mean13.48815029
Median Absolute Deviation (MAD)3.7
Skewness0.160060788
Sum4666.9
Variance24.03038092
MonotonicityNot monotonic
2022-04-13T22:56:50.333227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.87
 
2.0%
10.17
 
2.0%
11.46
 
1.7%
11.36
 
1.7%
8.86
 
1.7%
16.35
 
1.4%
13.65
 
1.4%
10.35
 
1.4%
17.75
 
1.4%
16.95
 
1.4%
Other values (153)289
83.5%
ValueCountFrequency (%)
3.32
0.6%
3.51
 
0.3%
4.11
 
0.3%
4.21
 
0.3%
4.51
 
0.3%
4.62
0.6%
4.71
 
0.3%
4.82
0.6%
5.13
0.9%
5.21
 
0.3%
ValueCountFrequency (%)
26.32
0.6%
24.31
0.3%
23.71
0.3%
23.51
0.3%
23.42
0.6%
23.31
0.3%
23.21
0.3%
22.91
0.3%
22.71
0.3%
22.61
0.3%

rhum
Real number (ℝ≥0)

HIGH CORRELATION

Distinct58
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.36705202
Minimum24
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-13T22:56:50.454763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile47.25
Q160
median69
Q376
95-th percentile88.75
Maximum98
Range74
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.48377317
Coefficient of variation (CV)0.1825992608
Kurtosis0.06790344061
Mean68.36705202
Median Absolute Deviation (MAD)8
Skewness-0.1725588781
Sum23655
Variance155.8445924
MonotonicityNot monotonic
2022-04-13T22:56:50.835944image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6914
 
4.0%
6713
 
3.8%
7213
 
3.8%
7313
 
3.8%
7113
 
3.8%
7913
 
3.8%
6313
 
3.8%
7412
 
3.5%
6412
 
3.5%
6811
 
3.2%
Other values (48)219
63.3%
ValueCountFrequency (%)
241
 
0.3%
361
 
0.3%
381
 
0.3%
391
 
0.3%
402
 
0.6%
412
 
0.6%
432
 
0.6%
445
1.4%
452
 
0.6%
471
 
0.3%
ValueCountFrequency (%)
982
 
0.6%
971
 
0.3%
962
 
0.6%
943
0.9%
913
0.9%
901
 
0.3%
896
1.7%
884
1.2%
876
1.7%
865
1.4%

wdsp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct20
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.043352601
Minimum2
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-13T22:56:51.039919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q16
median9
Q311
95-th percentile15.75
Maximum22
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.56811553
Coefficient of variation (CV)0.3945567189
Kurtosis0.6473410997
Mean9.043352601
Median Absolute Deviation (MAD)3
Skewness0.7274960821
Sum3129
Variance12.73144844
MonotonicityNot monotonic
2022-04-13T22:56:51.163798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
649
14.2%
737
10.7%
936
10.4%
834
9.8%
1034
9.8%
1228
8.1%
1126
7.5%
524
6.9%
416
 
4.6%
1316
 
4.6%
Other values (10)46
13.3%
ValueCountFrequency (%)
23
 
0.9%
35
 
1.4%
416
 
4.6%
524
6.9%
649
14.2%
737
10.7%
834
9.8%
936
10.4%
1034
9.8%
1126
7.5%
ValueCountFrequency (%)
222
 
0.6%
211
 
0.3%
201
 
0.3%
184
 
1.2%
172
 
0.6%
168
 
2.3%
156
 
1.7%
1414
4.0%
1316
4.6%
1228
8.1%

hour
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.58959538
Minimum0
Maximum23
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-13T22:56:51.277735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q111
median13
Q316
95-th percentile19
Maximum23
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.146529049
Coefficient of variation (CV)0.2315395684
Kurtosis0.168789465
Mean13.58959538
Median Absolute Deviation (MAD)2
Skewness0.1107479614
Sum4702
Variance9.900645053
MonotonicityNot monotonic
2022-04-13T22:56:51.362284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1252
15.0%
1146
13.3%
1341
11.8%
1830
8.7%
1530
8.7%
1027
7.8%
1727
7.8%
1426
7.5%
1624
6.9%
913
 
3.8%
Other values (7)30
8.7%
ValueCountFrequency (%)
01
 
0.3%
810
 
2.9%
913
 
3.8%
1027
7.8%
1146
13.3%
1252
15.0%
1341
11.8%
1426
7.5%
1530
8.7%
1624
6.9%
ValueCountFrequency (%)
231
 
0.3%
221
 
0.3%
211
 
0.3%
205
 
1.4%
1911
 
3.2%
1830
8.7%
1727
7.8%
1624
6.9%
1530
8.7%
1426
7.5%

day
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.10115607
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-13T22:56:51.453593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q322
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.525062716
Coefficient of variation (CV)0.5645304688
Kurtosis-1.146158321
Mean15.10115607
Median Absolute Deviation (MAD)7
Skewness-0.002382658239
Sum5225
Variance72.67669431
MonotonicityNot monotonic
2022-04-13T22:56:51.557188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1720
 
5.8%
1620
 
5.8%
318
 
5.2%
815
 
4.3%
215
 
4.3%
1314
 
4.0%
414
 
4.0%
1913
 
3.8%
1112
 
3.5%
2212
 
3.5%
Other values (20)193
55.8%
ValueCountFrequency (%)
110
2.9%
215
4.3%
318
5.2%
414
4.0%
56
 
1.7%
69
2.6%
711
3.2%
815
4.3%
95
 
1.4%
1012
3.5%
ValueCountFrequency (%)
308
2.3%
2911
3.2%
2810
2.9%
2711
3.2%
269
2.6%
2511
3.2%
2412
3.5%
2311
3.2%
2212
3.5%
219
2.6%

month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.953757225
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-13T22:56:51.654067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q38
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.519063615
Coefficient of variation (CV)0.4231048595
Kurtosis-0.9594381869
Mean5.953757225
Median Absolute Deviation (MAD)2
Skewness0.4300097308
Sum2060
Variance6.345681495
MonotonicityNot monotonic
2022-04-13T22:56:51.738620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
367
19.4%
466
19.1%
743
12.4%
641
11.8%
529
8.4%
1029
8.4%
828
8.1%
924
 
6.9%
1114
 
4.0%
23
 
0.9%
Other values (2)2
 
0.6%
ValueCountFrequency (%)
11
 
0.3%
23
 
0.9%
367
19.4%
466
19.1%
529
8.4%
641
11.8%
743
12.4%
828
8.1%
924
 
6.9%
1029
8.4%
ValueCountFrequency (%)
121
 
0.3%
1114
 
4.0%
1029
8.4%
924
 
6.9%
828
8.1%
743
12.4%
641
11.8%
529
8.4%
466
19.1%
367
19.4%

year
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2021
342 
2022
 
4

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021342
98.8%
20224
 
1.2%

Length

2022-04-13T22:56:51.831477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T22:56:51.889558image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2021342
98.8%
20224
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

holiday
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size598.0 B
False
346 
ValueCountFrequency (%)
False346
100.0%
2022-04-13T22:56:51.924589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

dayofweek_n
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.502890173
Minimum0
Maximum6
Zeros32
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-13T22:56:51.975192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.938546527
Coefficient of variation (CV)0.5534134475
Kurtosis-1.099432829
Mean3.502890173
Median Absolute Deviation (MAD)1
Skewness-0.3969883327
Sum1212
Variance3.757962637
MonotonicityNot monotonic
2022-04-13T22:56:52.061313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
584
24.3%
656
16.2%
454
15.6%
141
11.8%
341
11.8%
238
11.0%
032
 
9.2%
ValueCountFrequency (%)
032
 
9.2%
141
11.8%
238
11.0%
341
11.8%
454
15.6%
584
24.3%
656
16.2%
ValueCountFrequency (%)
656
16.2%
584
24.3%
454
15.6%
341
11.8%
238
11.0%
141
11.8%
032
 
9.2%

dayofweek
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size830.0 B
Saturday
84 
Sunday
56 
Friday
54 
Thursday
41 
Tuesday
41 
Other values (2)
70 

Length

Max length9
Median length7
Mean length7.170520231
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonday
2nd rowTuesday
3rd rowWednesday
4th rowThursday
5th rowFriday

Common Values

ValueCountFrequency (%)
Saturday84
24.3%
Sunday56
16.2%
Friday54
15.6%
Thursday41
11.8%
Tuesday41
11.8%
Wednesday38
11.0%
Monday32
 
9.2%

Length

2022-04-13T22:56:52.161479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T22:56:52.231013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
saturday84
24.3%
sunday56
16.2%
friday54
15.6%
thursday41
11.8%
tuesday41
11.8%
wednesday38
11.0%
monday32
 
9.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

working_day
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size598.0 B
True
206 
False
140 
ValueCountFrequency (%)
True206
59.5%
False140
40.5%
2022-04-13T22:56:52.290762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

season
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size678.0 B
Spring
138 
Summer
101 
Winter
54 
Autumn
53 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter
2nd rowWinter
3rd rowWinter
4th rowWinter
5th rowWinter

Common Values

ValueCountFrequency (%)
Spring138
39.9%
Summer101
29.2%
Winter54
 
15.6%
Autumn53
 
15.3%

Length

2022-04-13T22:56:52.357643image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T22:56:52.422446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
spring138
39.9%
summer101
29.2%
winter54
 
15.6%
autumn53
 
15.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

peak
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size598.0 B
False
244 
True
102 
ValueCountFrequency (%)
False244
70.5%
True102
29.5%
2022-04-13T22:56:52.468760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

timesofday
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size678.0 B
Afternoon
200 
Morning
96 
Evening
48 
Night
 
2

Length

Max length9
Median length9
Mean length8.144508671
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEvening
2nd rowEvening
3rd rowAfternoon
4th rowMorning
5th rowAfternoon

Common Values

ValueCountFrequency (%)
Afternoon200
57.8%
Morning96
27.7%
Evening48
 
13.9%
Night2
 
0.6%

Length

2022-04-13T22:56:52.547611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T22:56:52.617455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
afternoon200
57.8%
morning96
27.7%
evening48
 
13.9%
night2
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rain_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size686.0 B
no rain
341 
drizzle
 
2
moderate rain
 
2
light rain
 
1

Length

Max length13
Median length7
Mean length7.043352601
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowno rain
2nd rowno rain
3rd rowno rain
4th rowno rain
5th rowno rain

Common Values

ValueCountFrequency (%)
no rain341
98.6%
drizzle2
 
0.6%
moderate rain2
 
0.6%
light rain1
 
0.3%

Length

2022-04-13T22:56:52.698178image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T22:56:52.763729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
rain344
49.9%
no341
49.4%
drizzle2
 
0.3%
moderate2
 
0.3%
light1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

count_hour
Real number (ℝ≥0)

Distinct13
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.86416185
Minimum12
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-13T22:56:52.828600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12
Q112
median13
Q315
95-th percentile18.75
Maximum26
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.262879791
Coefficient of variation (CV)0.1632179295
Kurtosis4.932132174
Mean13.86416185
Median Absolute Deviation (MAD)1
Skewness1.947849596
Sum4797
Variance5.120624948
MonotonicityNot monotonic
2022-04-13T22:56:52.918339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
12112
32.4%
1391
26.3%
1450
14.5%
1532
 
9.2%
1619
 
5.5%
1716
 
4.6%
188
 
2.3%
198
 
2.3%
205
 
1.4%
242
 
0.6%
Other values (3)3
 
0.9%
ValueCountFrequency (%)
12112
32.4%
1391
26.3%
1450
14.5%
1532
 
9.2%
1619
 
5.5%
1716
 
4.6%
188
 
2.3%
198
 
2.3%
205
 
1.4%
211
 
0.3%
ValueCountFrequency (%)
261
 
0.3%
242
 
0.6%
231
 
0.3%
211
 
0.3%
205
 
1.4%
198
 
2.3%
188
 
2.3%
1716
4.6%
1619
5.5%
1532
9.2%

count_day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct74
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.7254335
Minimum57
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-04-13T22:56:53.034259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile81.25
Q1107.25
median123
Q3134
95-th percentile163
Maximum171
Range114
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation23.43062014
Coefficient of variation (CV)0.1924874651
Kurtosis-0.288604986
Mean121.7254335
Median Absolute Deviation (MAD)13.5
Skewness0.01770175112
Sum42117
Variance548.99396
MonotonicityNot monotonic
2022-04-13T22:56:53.161217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13019
 
5.5%
13716
 
4.6%
12311
 
3.2%
12611
 
3.2%
11511
 
3.2%
13411
 
3.2%
11710
 
2.9%
11110
 
2.9%
12410
 
2.9%
1079
 
2.6%
Other values (64)228
65.9%
ValueCountFrequency (%)
571
 
0.3%
701
 
0.3%
711
 
0.3%
721
 
0.3%
734
1.2%
741
 
0.3%
761
 
0.3%
781
 
0.3%
791
 
0.3%
801
 
0.3%
ValueCountFrequency (%)
1717
2.0%
1687
2.0%
1637
2.0%
1623
0.9%
1577
2.0%
1557
2.0%
1545
1.4%
1536
1.7%
1514
1.2%
1485
1.4%

Interactions

2022-04-13T22:56:48.109698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:36.198629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:37.784043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:38.956858image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:40.093537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:41.378492image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:42.517560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:43.649151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:44.823125image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:46.098776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:47.091867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:48.195336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:36.304102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:37.883903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:39.050915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:40.384094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:41.472519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:42.604098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:43.736668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:44.909511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:46.182830image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:47.183317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:48.284804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:36.610384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:37.988623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:39.153194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:40.497160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:41.581840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:42.694417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:43.840071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:45.003523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:46.270188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:47.274003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:48.370851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:36.721773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:38.088609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:39.274671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:40.593702image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:41.683422image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:42.783193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:43.929350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:45.296067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:46.353115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:47.359628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:48.462388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:36.981248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:38.188124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:39.389911image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:40.691434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:41.793136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:42.883356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:44.055203image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:45.412392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:46.445778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:47.455232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:48.559748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:37.162552image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:38.306050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:39.495777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:40.792159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:41.897721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:42.987318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:44.163905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:45.518559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:46.546013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:47.560559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:48.643418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:37.269695image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:38.407167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:39.589224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:40.881080image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:41.990388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:43.080292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:44.269482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:45.610996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:46.633574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:47.646805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:48.728317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:37.374914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:38.536548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:39.685397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:40.976181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:42.103178image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:43.195445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:44.388243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:45.708656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:46.724619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:47.738112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:48.812897image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:37.472641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:38.640278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:39.786789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:41.077703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:42.210030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:43.314480image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:44.499729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:45.803512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:46.812578image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:47.829940image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:48.902993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:37.578017image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:38.747835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:39.904829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:41.180744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:42.308751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:43.436524image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:44.611286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:45.899041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:46.900492image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:47.923713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:48.997296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:37.683104image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:38.854952image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:40.004879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:41.285102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:42.411919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:43.556767image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:44.709611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:46.009666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:46.995824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-13T22:56:48.020241image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-04-13T22:56:53.278136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-13T22:56:53.431942image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-13T22:56:53.575183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-13T22:56:53.711884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-13T22:56:53.852932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-13T22:56:49.202315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-13T22:56:49.510796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexdateraintemprhumwdsphourdaymonthyearholidaydayofweek_ndayofweekworking_dayseasonpeaktimesofdayrain_typecount_hourcount_day
0182021-03-010.05.391818132021False0MondayTrueWinterTrueEveningno rain14107
1422021-03-020.04.288618232021False1TuesdayTrueWinterTrueEveningno rain14105
2602021-03-030.05.187812332021False2WednesdayTrueWinterFalseAfternoonno rain14107
3832021-03-040.04.581911432021False3ThursdayTrueWinterFalseMorningno rain1493
41122021-03-050.04.659616532021False4FridayTrueWinterTrueAfternoonno rain12120
51292021-03-060.04.16429632021False5SaturdayFalseWinterFalseMorningno rain13137
61312021-03-060.05.557711632021False5SaturdayFalseWinterFalseMorningno rain14137
71322021-03-060.06.757512632021False5SaturdayFalseWinterFalseAfternoonno rain13137
81332021-03-060.06.856713632021False5SaturdayFalseWinterFalseAfternoonno rain12137
91352021-03-060.06.558915632021False5SaturdayFalseWinterFalseAfternoonno rain14137

Last rows

df_indexdateraintemprhumwdsphourdaymonthyearholidaydayofweek_ndayofweekworking_dayseasonpeaktimesofdayrain_typecount_hourcount_day
33661822021-11-130.011.68951413112021False5SaturdayFalseAutumnFalseAfternoonno rain13115
33762272021-11-150.010.88751115112021False0MondayTrueAutumnFalseMorningno rain1295
33862282021-11-150.010.88771215112021False0MondayTrueAutumnFalseAfternoonno rain1295
33962722021-11-170.07.7809817112021False2WednesdayTrueAutumnTrueMorningno rain1493
34063972021-11-220.07.77661322112021False0MondayTrueAutumnFalseAfternoonno rain1273
34166882021-12-040.03.58517164122021False5SaturdayFalseAutumnFalseAfternoonno rain1292
34278182022-01-200.05.1794182012022False3ThursdayTrueWinterTrueEveningno rain1281
34381542022-02-030.011.0731218322022False3ThursdayTrueWinterTrueEveningno rain12118
34481772022-02-040.03.3851417422022False4FridayTrueWinterTrueAfternoonno rain1385
34583872022-02-130.08.8894111322022False6SundayFalseWinterFalseMorningno rain1273